Search Results (88)

Multimodal named entity recognition (MNER) leverages both textual and visual information to improve entity recognition, particularly in unstructured scenarios such as social media. While existing approaches predominantly rely on raster images (e.g., JPEG, PNG), scalable vector graphics (SVG) offer unique advantages in resolution independence and structured semantic representation—an underexplored potential in multimodal learning. To fill this gap, we propose MNER-SVG, the first framework that incorporates SVG as a visual modality and enhances it with ChatGPT-generated auxiliary knowledge. Specifically, we introduce a Multimodal Similar Instance Perception Module that retrieves semantically relevant examples and prompts ChatGPT to generate contextual explanations. We further construct a Full-Text Graph and a Multimodal Interaction Graph, which are processed via Graph Attention Networks (GATs) to achieve fine-grained cross-modal alignment and feature fusion. Finally, a Conditional Random Field (CRF) layer is employed for structured decoding. To support evaluation, we present SvgNER, the first MNER dataset annotated with SVG-specific visual content. Extensive experiments demonstrate that MNER-SVG achieves state-of-the-art performance with an F1 score of 82.23%, significantly outperforming both text-only and existing multimodal baselines. This work validates the feasibility and potential of integrating vector graphics and large language model-generated knowledge into multimodal NER, opening a new research direction for structured visual semantics in fine-grained multimodal understanding. Full article

Human resource (HR) departments in small and medium enterprises face challenges such as high operational costs, regulatory compliance, and routine task management, compounded by limited computing resources and data privacy concerns. To address these issues, we introduce a lightweight, on-premises language solution using a fine-tuned TinyLlama model integrated with a retrieval-augmented generation model for HR applications. Leveraging parameter-efficient methods, such as low-rank adaptation, the model shows excellent performance with a single graphics processing unit. The retrieval system is accurate in accessing local legal documents, complying with Malaysia’s regulations, while preserving data sovereignty. This approach provides SMEs with cost-effective, transparent, and scalable HR support. Full article

This paper explores the feasibility of employing small language models (SLMs) on edge devices powered by batteries in environments with limited/no internet connectivity. SLMs in fact offer significant advantages in such scenarios due to their lower resource requirements with respect to large language models. The use case in this study is maritime navigation—in particular, the documentation on Sailing Directions (Enroutd) of the World Port Index (WPI) provided by the National Geospatial-Intelligence Agency (NGA), which provides information that cannot be shown graphically on nautical charts and is not readily available elsewhere. In this environment, response immediacy is not critical, as users have sufficient time to query information while navigating and planning activities, making edge devices ideal for running these models. On the contrary, the response quality is fundamental. For this reason, given the constrained knowledge of SLMs in maritime contexts, we investigate the use of the retrieval-augmented generation (RAG) methodology, integrating external information from sailing directions. A comparative analysis is presented to evaluate the performance of various state-of-the-art SLMs, focusing on response quality, the effectiveness of the RAG component, and inference times. Full article

In recent years, people have increasingly sought to generate exercise trajectories that embody specific semantic shapes in order to create GPS art and share it on social platforms. This trend has created an urgent demand for navigation paths with specific semantic meanings on smartwatches and smartphones. Current methods mainly rely on manual design and lack efficient automation. Therefore, this study proposes a novel method for automatically obtaining navigation paths with specified shapes by retrieving graphics similar to the input graphic shape from the road network. This method uses invariant spatial relationships, such as turning angles and length ratios, along with graph matching techniques to establish one-to-one or one-to-many correspondences between line segments in the input individual graphics and those in the road network. This enables the retrieval of individual graphics within the road network. Based on this, a greedy strategy-based algorithm is proposed to solve the combined graphics retrieval problem. The results are evaluated to ensure high quality. The accuracy and effectiveness of our method are validated through experimental results using simulated and real road network data from five different regions. Furthermore, shape-constrained graphics retrieval expands the application domain of spatial scene matching. Full article

This paper investigates the global polynomial synchronization (GPS) problem for quaternion-valued neural networks (QVNNs) featuring proportional delay, parameter uncertainty, and external disturbance. A combined approach of sliding mode control (SMC) and a non-separation strategy is adopted to achieve this goal. First, an integral-type sliding surface is designed for the system. Then, by constructing a delay-free Lyapunov functional and leveraging the properties of the quaternion vector norm and inequality techniques, sufficient conditions are derived to achieve GPS for the sliding mode dynamics. Furthermore, both a SMC law and an adaptive SMC law are designed, with a reachability analysis confirming that the system trajectories reach the predefined sliding surface in finite time. Finally, numerical examples with graphical analysis are provided to verify the obtained results, along with their application in color image pattern storage and retrieval. Full article

The One Health framework highlights the interconnectedness of human, animal, and environmental health, requiring interdisciplinary and multisectoral collaboration to address complex global health challenges. This scoping review protocol aims to guide the systematic mapping on how studies and policy initiatives have incorporated socioecological interconnections within the One Health paradigm, following the Joanna Briggs Institute guidance and the PRISMA Scr checklist. The experimental design includes searches in PubMed, Scopus, Web of Science, LILACS, Health Systems Evidence, Social Systems Evidence, and Google Scholar for the period from 2004 to 2025. The strategy, developed with librarian support and peer reviewed, includes terms in English, Portuguese, and Spanish. Pilot searches retrieved 5333 PubMed and 470 LILACS records. Eligible documents must explicitly present two or more of the six One Health dimensions: policies to strengthen health systems; antimicrobial resistance; food safety; environmental health; emerging and re-emerging zoonotic epidemics and pandemics; endemic zoonotic, neglected tropical and vector-borne diseases. A standardized tool was developed for data extraction, synthesizing in narrative, tabular, and graphical formats. The protocol’s utilization will provide comprehensive mapping of practices and policies, identifying achievements, barriers, and knowledge gaps to inform future strategies and strengthen global health governance. Full article

Dynamic stress data are essential for evaluating structural fatigue life. To address the challenges of complex test data formats, low data reading efficiency, and insufficient visualization, this study systematically analyzes the .raw and .sie file formats from IMC and HBM data acquisition systems and proposes a unified parsing approach. A lightweight .dac format is designed, featuring a “single-channel–single-file” storage strategy that enables rapid, independent retrieval of specific channels and seamless cross-platform sharing, effectively eliminating the inefficiency of the .sie format caused by multi-channel coupling. Based on Python v3.11, an automated format conversion tool and a PyQt5-based visualization platform are developed, integrating graphical plotting, interactive operations, and fatigue strength evaluation functions. The platform supports stress feature extraction, rainflow counting, Goodman correction, and full life-cycle fatigue damage assessment based on the Palmgren–Miner rule. Experimental results demonstrate that the proposed system accurately reproduces both time- and frequency-domain features, with equivalent stress deviations within 2% of nCode results, and achieves a 7–8× improvement in file loading speed compared with the original format. Furthermore, multi-channel scalability tests confirm a linear increase in conversion time (R² > 0.98) and stable throughput across datasets up to 10.20 GB, demonstrating strong performance consistency for large-scale engineering data. The proposed approach establishes a reliable data foundation and efficient analytical tool for fatigue life assessment of structures under complex operating conditions. Full article

Accurate and efficient rock mass characterization is essential in geotechnical engineering, yet traditional tunnel face mapping remains time consuming, subjective, and potentially hazardous. Recent advances in digital technologies and AI offer automation opportunities, but many existing solutions are hindered by slow 3D scanning, computationally intensive processing, and limited integration flexibility. This paper presents Tunnel Rapid AI Classification (TRaiC), an open-source MATLAB-based platform for rapid and automated tunnel face mapping. TRaiC integrates single-shot 360° panoramic photography, AI-powered discontinuity detection, 3D textured digital twin generation, rock mass discontinuity characterization, and Retrieval-Augmented Generation with Large Language Models (RAG-LLM) for automated geological interpretation and standardized reporting. The modular eight-stage workflow includes simplified 3D modeling, trace segmentation, 3D joint network analysis, and rock mass classification using RMR, with outputs optimized for Geo-BIM integration. Initial evaluations indicate substantial reductions in processing time and expert assessment workload. Producing a lightweight yet high-fidelity digital twin, TRaiC enables computational efficiency, transparency, and reproducibility, serving as a foundation for future AI-assisted geotechnical engineering research. Its graphical user interface and well-structured open-source code make it accessible to users ranging from beginners to advanced researchers. Full article

Metaverse Recordings (MVRs), screen recordings of user experiences in virtual environments, represent a mostly underexplored field. This article addresses the integration of MVR and Multimedia Information Retrieval (MMIR). Unlike conventional media, MVRs can include additional streams of structured data, such as Scene Raw Data (SRD) and Peripheral Data (PD), which capture graphical rendering states and user interactions. We explore the technical facets of recordings in the Metaverse, detailing diverse methodologies and their implications for MVR-specific Multimedia Information Retrieval. Our discussion not only highlights the unique opportunities of MVR content analysis, but also examines the challenges they pose to conventional MMIR paradigms. It addresses the key challenges around the semantic gap in existing content analysis tools when applied to MVRs and the high computational cost and limited recall of video-based feature extraction. We present a model for MVR structure, a prototype recording system, and an evaluation framework to assess retrieval performance. We collected a set of 111 MVRs to study and evaluate the intricacies. Our findings show that SRD and PD provide significant, low-cost contributions to retrieval accuracy and scalability, and support the case for integrating structured interaction data into future MMIR architectures. Full article

The Content-Centric Network (CCN) presents an alternative to the conventional TCP/IP network, where IP is fundamental for communication between the source and destination. Instead of relying on IP addresses, CCN emphasizes content to enable efficient data distribution through caching and delivery. The increasing demand of graphic-intensive applications requires minimal response time and optimized resource utilization. Therefore, the CCN plays a vital role due to its efficient architecture and content management approach. To reduce data retrieval delays in CCNs, traditional methods improve caching mechanisms through clustering. However, these methods do not address the optimal use of resources, including CPU, memory, storage, and available links, along with the incorporation of social awareness. This study proposes SARAC4N, a socially and resource-aware caching framework for clustered Content-Centric Networks that integrates dual-head clustering and popularity-driven content placement. It enhances caching efficiency, reduces retrieval delays, and improves resource utilization across heterogeneous network topologies. This approach will help resolve congestion issues while enhancing social awareness, lowering error rates, and ensuring efficient content delivery. The proposed Socially and Resource-Aware Caching in Clustered Content-Centric Network (SARAC4N) enhances caching effectiveness by optimally utilizing resources and positioning them with social awareness within the cluster. Furthermore, it enhances metrics such as data retrieval time, reduces computation and memory usage, minimizes data redundancy, optimizes network usage, and lowers storage requirements, all while maintaining a very low error rate. Full article

Introduction: The health education curricula should explicitly recognize, define, and address the unique needs and health disparities faced by Black and LGBTQIA+ populations, as a means of ensuring that healthcare for these populations is both comprehensive and inclusive. Aim: To map scientific evidence and identify knowledge gaps regarding the health of Black and LGBTQIA+ populations within the global context of health education. Methods: A scoping review will be conducted following the JBI methodology. The articles will be retrieved from Scopus, Web of Science, PubMed, Embase, MEDLINE, BVS, CINAHL, ERIC, Cochrane, BDTD, PQDT, EBSCO, and NDLTD. The search will be conducted without language or time restrictions. Two independent reviewers will screen the studies and extract data using a form specifically developed for this purpose. The concepts, definitions, structures, results, and applications of professional health education worldwide for the healthcare of Black and LGBTQIA+ populations will be summarized and discussed. Inclusion Criteria: Studies related to professional health training at both undergraduate and graduate levels, as well as other educational modalities that address the provision of healthcare for these populations, will be included. The results will be presented in both tabular and graphical formats, accompanied by a narrative summary. Protocol registered in the Open Science Framework (OSF). Full article

Urban documents like city planning reports and environmental data often feature complex charts and texts that require effective summarization tools, particularly in smart city management systems. These documents increasingly use graphical abstracts alongside textual summaries to enhance readability, making automated abstract generation crucial. This study explores the application of summarization technology using scientific paper abstract generation as a case. The challenge lies in processing the longer multimodal content typical in research papers. To address this, a deep multimodal-interactive network is proposed for accurate document summarization. This model enhances structural information from both images and text, using a combination module to learn the correlation between them. The integrated model aids both summary generation and significant image selection. For the evaluation, a dataset is created that encompasses both textual and visual components along with structural information, such as the coordinates of the text and the layout of the images. While primarily focused on abstract generation and image selection, the model also supports text–image cross-modal retrieval. Experimental results on the proprietary dataset demonstrate that the proposed method substantially outperforms both extractive and abstractive baselines. In particular, it achieves a Rouge-1 score of 46.55, a Rouge-2 score of 16.13, and a Rouge-L score of 24.95, improving over the best comparison abstractive model (Pegasus: Rouge-1 43.63, Rouge-2 14.62, Rouge-L 24.46) by approximately 2.9, 1.5, and 0.5 points, respectively. Even against strong extractive methods like TextRank (Rouge-1 30.93) and LexRank (Rouge-1 29.63), our approach shows gains of over 15 points in Rouge-1, underlining its effectiveness in capturing both textual and visual semantics. These results suggest significant potential for smart city applications—such as accident scene documentation and automated environmental monitoring summaries—where rapid, accurate processing of urban multimodal data is essential. Full article

This work presents an optical encryption process for various types of information related to 3D worlds (scenes) or 2D images, utilizing Computer-Generated Holograms (CGHs). It also introduces a modification to the Dual Random Phase Encoding (DRPE) encryption algorithm by incorporating pixel shuffling. This proposal enables the use of either a single key for both pixel shuffling and phase mask definition or two independent keys. The latter option is particularly advantageous in applications that require the involvement of two independent agents to retrieve the original plaintext. The dimension of the CGHs determines the size of the keys based on the random generation of values by cryptographically secure algorithms, so the use of arithmetic encryption is proposed for data compression. However, this proposal allows the use of other algorithms described in the literature to generate the shuffle and phase matrices. The complete workflow is described starting from the synthesis of a 3D scene, defined by a mesh of triangles with shape and appearance modeling, or 2D images of any level of geometric or visual complexity using computer graphics; its storage in a CGH, the encryption and decryption process, and finally, the results obtained in the laboratory and by simulation are shown. The similarity between different encryption levels is measured by the Pearson Coefficient to evaluate the results obtained. Full article

The current research presents a practical approach to assess energy performance and thermal comfort in households through monitoring campaigns. The campaigns are conducted in a Greek city, involving the installation of low-intrusive recording devices for hourly electricity consumption, indoor temperature, and relative humidity in different residences in winter and summer periods. The recorded indoor environmental conditions are initially compiled to the Predicted Mean Vote (PMV) index, followed by the formulation of databases of hourly electricity consumption, PMV and local outdoor climate conditions retrieved by an official source of meteorological conditions. A special algorithm for database processing was developed which takes into account the eligibility of data series, i.e., only the ones corresponding to non-zero electricity consumption are treated as eligible. First, the sequential temporal progress of energy consumption and thermal comfort is produced towards the assessment of energy-use intensity and thermal comfort patterns. Secondly, through summing of the electricity consumption within 0.5-step PMV intervals, under three outdoor temperature intervals with approximately the same number of eligible measurements, reliable interrelations of energy consumption and PMV are obtained even for residences with limited amount of measured data. It is revealed that the weekly electricity consumption ranged within 0.15–3.59 kWh/m² for the winter cases and within 0.29–1.72 kWh/m² for the summer cases. The acceptable range of −1 ≤ PMV ≤ 1 interval holds an occurrence frequency from 69.46% to 93.39% and from 37.94% to 70.31% for the winter and summer examined cases, respectively. Less resistance to discomfort conditions is observed at most of the summer examined households exhibiting the electricity peak within the 1 ≤ PMV ≤ 1.5 interval, contrary to the winter cases for which the electricity peak occurred within the −1 ≤ PMV ≤ −0.5 interval. The study provides graphical relationships of PMV and electricity consumption under various outdoor temperatures paving the way for correlating thermal comfort and energy consumption. Full article

This research introduces novel analytical methods for evaluating multi-UAV persistent search and retrieval with stochastic target appearance (PSR-STA) scenarios. Traditional approaches that rely on single aggregate effectiveness measures for a scenario fail to capture the complex spatiotemporal dynamics of multi-UAV operations and provide limited insights into improving search performance. To address these limitations, we present a comprehensive analysis framework combining temporal and spatial analysis techniques. For temporal analysis, we employ a graphical comparison of line charts and discrete Fourier transform analysis to identify shared temporal patterns across scenarios. Spatial patterns are analyzed through principal components analysis and random forest surrogate modeling with profiling to understand non-linear parameter influences. Additionally, we introduce trellis charts for integrated visualization and analysis of combined spatiotemporal patterns. This research builds on a case study developed in a previous case study of multi-UAV PSR-STA. While the previous work established foundational algorithms and metrics for multi-UAV PSR-STA, this study introduces sophisticated spatiotemporal analysis techniques that reveal deep insights into system behavior and enable a nuanced understanding of UAV search performance across varied scenarios. Full article